UfsCov: UfsCov algorithm for unsupervised feature selection

Description Usage Arguments Details Value Note Author(s) References Examples

Description

Applies the UfsCov algorithm based on the space filling concept, by using a sequatial forward search (SFS).

Usage

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Arguments

data

Data of class: matrix or data.frame.

Details

Since the algorithm is based on pairwise distances, and according to the computing power of your machine, large number of data points can take much time and needs more memory. See UfsCov_par for parellel computing, or UfsCov_ff for memory efficient storage of large data on disk and fast access (by using the ff and the ffbase packages).

Value

A list of two elements:

Note

The algorithm does not deal with missing values and constant features. Please make sure to remove them.

Author(s)

Mohamed Laib Mohamed.Laib@unil.ch

References

M. Laib and M. Kanevski (2017). Unsupervised Feature Selection Based on Space Filling Concept, arXiv:1706.08894.

Examples

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infinity<-Infinity(n=800)
Results<- UfsCov(infinity)

cou<-colnames(infinity)
nom<-cou[Results[[2]]]
par(mfrow=c(1,1), mar=c(5,5,2,2))
names(Results[[1]])<-cou[Results[[2]]]
plot(Results[[1]] ,pch=16,cex=1,col="blue", axes = FALSE,
xlab = "Added Features", ylab = "Coverage measure")
lines(Results[[1]] ,cex=2,col="blue")
grid(lwd=1.5,col="gray" )
box()
axis(2)
axis(1,1:length(nom),nom)
which.min(Results[[1]])

## Not run: 

#### UfsCov on the Butterfly dataset ####
require(IDmining)

N <- 1000
raw_dat <- Butterfly(N)
dat<-raw_dat[,-9]

Results<- UfsCov(dat)
cou<-colnames(dat)
nom<-cou[Results[[2]]]
par(mfrow=c(1,1), mar=c(5,5,2,2))
names(Results[[1]])<-cou[Results[[2]]]

plot(Results[[1]] ,pch=16,cex=1,col="blue", axes = FALSE,
xlab = "Added Features", ylab = "Coverage measure")
lines(Results[[1]] ,cex=2,col="blue")
grid(lwd=1.5,col="gray" )
box()
axis(2)
axis(1,1:length(nom),nom)
which.min(Results[[1]])


## End(Not run)

Example output

================================================================================x2 
 2 
Loading required package: IDmining
================================================================================X1 
 3 

SFtools documentation built on May 2, 2019, 11:02 a.m.